mirror of
https://github.com/katanemo/plano.git
synced 2026-06-17 15:25:17 +02:00
Merge branch 'main' into salmanap/fix-docs-037
This commit is contained in:
Salman Paracha
GitHub
commit
b09701becc
25 changed files with 290 additions and 312 deletions
2
.github/workflows/e2e_archgw.yml
vendored
2
.github/workflows/e2e_archgw.yml
vendored
|
|
@ -24,7 +24,7 @@ jobs:
|
|||
|
||||
- name: build arch docker image
|
||||
run: |
|
||||
cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.4 -t katanemo/archgw:latest
|
||||
cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.7 -t katanemo/archgw:latest
|
||||
|
||||
- name: start archgw
|
||||
env:
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ jobs:
|
|||
|
||||
- name: build arch docker image
|
||||
run: |
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.4
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.7
|
||||
|
||||
- name: install poetry
|
||||
run: |
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ jobs:
|
|||
|
||||
- name: build arch docker image
|
||||
run: |
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.4
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.7
|
||||
|
||||
- name: install poetry
|
||||
run: |
|
||||
|
|
|
|||
2
.github/workflows/validate_arch_config.yml
vendored
2
.github/workflows/validate_arch_config.yml
vendored
|
|
@ -24,7 +24,7 @@ jobs:
|
|||
|
||||
- name: build arch docker image
|
||||
run: |
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.4
|
||||
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.7
|
||||
|
||||
- name: validate arch config
|
||||
run: |
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
|
|||
```console
|
||||
$ python3.12 -m venv venv
|
||||
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
|
||||
$ pip install archgw==0.3.4
|
||||
$ pip install archgw==0.3.7
|
||||
```
|
||||
|
||||
### Build Agentic Apps with Arch Gateway
|
||||
|
|
@ -148,7 +148,7 @@ endpoints:
|
|||
```sh
|
||||
|
||||
$ archgw up arch_config.yaml
|
||||
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.4
|
||||
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.7
|
||||
2024-12-05 16:56:28,485 - cli.utils - INFO - Schema validation successful!
|
||||
2024-12-05 16:56:28,485 - cli.main - INFO - Starting arch model server and arch gateway
|
||||
2024-12-05 16:56:51,647 - cli.core - INFO - Container is healthy!
|
||||
|
|
|
|||
|
|
@ -2,14 +2,14 @@
|
|||
nodaemon=true
|
||||
|
||||
[program:brightstaff]
|
||||
command=sh -c "RUST_LOG=info /app/brightstaff 2>&1 | tee /var/log/brightstaff.log"
|
||||
command=sh -c "RUST_LOG=debug /app/brightstaff 2>&1 | tee /var/log/brightstaff.log"
|
||||
stdout_logfile=/dev/stdout
|
||||
redirect_stderr=true
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile_maxbytes=0
|
||||
|
||||
[program:envoy]
|
||||
command=/bin/sh -c "python /app/config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --component-log-level wasm:info 2>&1 | tee /var/log//envoy.log"
|
||||
command=/bin/sh -c "python /app/config_generator.py && envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && envoy -c /etc/envoy.env_sub.yaml --component-log-level wasm:debug 2>&1 | tee /var/log//envoy.log"
|
||||
stdout_logfile=/dev/stdout
|
||||
redirect_stderr=true
|
||||
stdout_logfile_maxbytes=0
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ source venv/bin/activate
|
|||
|
||||
### Step 3: Run the build script
|
||||
```bash
|
||||
pip install archgw==0.3.4
|
||||
pip install archgw==0.3.7
|
||||
```
|
||||
|
||||
## Uninstall Instructions: archgw CLI
|
||||
|
|
|
|||
|
|
@ -10,4 +10,4 @@ SERVICE_NAME_MODEL_SERVER = "model_server"
|
|||
SERVICE_ALL = "all"
|
||||
MODEL_SERVER_LOG_FILE = "~/archgw_logs/modelserver.log"
|
||||
ARCHGW_DOCKER_NAME = "archgw"
|
||||
ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.4")
|
||||
ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.7")
|
||||
|
|
|
|||
12
arch/tools/poetry.lock
generated
12
arch/tools/poetry.lock
generated
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
[[package]]
|
||||
name = "archgw_modelserver"
|
||||
version = "0.3.4"
|
||||
version = "0.3.7"
|
||||
description = "A model server for serving models"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
|
@ -104,13 +104,13 @@ i18n = ["Babel (>=2.7)"]
|
|||
|
||||
[[package]]
|
||||
name = "jsonschema"
|
||||
version = "4.24.0"
|
||||
version = "4.25.0"
|
||||
description = "An implementation of JSON Schema validation for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "jsonschema-4.24.0-py3-none-any.whl", hash = "sha256:a462455f19f5faf404a7902952b6f0e3ce868f3ee09a359b05eca6673bd8412d"},
|
||||
{file = "jsonschema-4.24.0.tar.gz", hash = "sha256:0b4e8069eb12aedfa881333004bccaec24ecef5a8a6a4b6df142b2cc9599d196"},
|
||||
{file = "jsonschema-4.25.0-py3-none-any.whl", hash = "sha256:24c2e8da302de79c8b9382fee3e76b355e44d2a4364bb207159ce10b517bd716"},
|
||||
{file = "jsonschema-4.25.0.tar.gz", hash = "sha256:e63acf5c11762c0e6672ffb61482bdf57f0876684d8d249c0fe2d730d48bc55f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -121,7 +121,7 @@ rpds-py = ">=0.7.1"
|
|||
|
||||
[package.extras]
|
||||
format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
|
||||
format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
|
||||
format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "jsonschema-specifications"
|
||||
|
|
@ -576,4 +576,4 @@ files = [
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "d1e256d1bb05fab98731edc11ea15391e9611acf2c3461d00d25c5fa0040de23"
|
||||
content-hash = "1875c613e62e116d557ad2d30491891557b4114a99c7c65b22b26d690e9e268b"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "archgw"
|
||||
version = "0.3.4"
|
||||
version = "0.3.7"
|
||||
description = "Python-based CLI tool to manage Arch Gateway."
|
||||
authors = ["Katanemo Labs, Inc."]
|
||||
packages = [
|
||||
|
|
@ -10,7 +10,7 @@ readme = "README.md"
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
archgw_modelserver = "^0.3.4"
|
||||
archgw_modelserver = "^0.3.7"
|
||||
click = "^8.1.7"
|
||||
jinja2 = "^3.1.4"
|
||||
jsonschema = "^4.23.0"
|
||||
|
|
|
|||
|
|
@ -32,6 +32,8 @@ pub async fn chat_completions(
|
|||
|
||||
let chat_request_bytes = request.collect().await?.to_bytes();
|
||||
|
||||
debug!("Received request body (raw utf8): {}", String::from_utf8_lossy(&chat_request_bytes));
|
||||
|
||||
let chat_request_parsed = serde_json::from_slice::<serde_json::Value>(&chat_request_bytes)
|
||||
.inspect_err(|err| {
|
||||
warn!(
|
||||
|
|
|
|||
|
|
@ -1,3 +1,2 @@
|
|||
pub mod chat_completions;
|
||||
pub mod models;
|
||||
pub mod preferences;
|
||||
|
|
|
|||
|
|
@ -1,135 +0,0 @@
|
|||
use bytes::Bytes;
|
||||
use common::configuration::{LlmProvider, ModelUsagePreference};
|
||||
use http_body_util::{combinators::BoxBody, BodyExt, Full};
|
||||
use hyper::{Request, Response, StatusCode};
|
||||
use serde_json;
|
||||
use std::{collections::HashMap, sync::Arc};
|
||||
use tracing::{info, warn};
|
||||
|
||||
pub async fn list_preferences(
|
||||
llm_providers: Arc<tokio::sync::RwLock<Vec<LlmProvider>>>,
|
||||
) -> Response<BoxBody<Bytes, hyper::Error>> {
|
||||
let prov = llm_providers.read().await;
|
||||
// convert the LlmProvider to UsageBasedProvider
|
||||
let providers_with_usage = prov
|
||||
.iter()
|
||||
.map(|provider| ModelUsagePreference {
|
||||
name: provider.name.clone(),
|
||||
model: provider.model.clone().unwrap_or_default(),
|
||||
usage: provider.usage.clone(),
|
||||
})
|
||||
.collect::<Vec<ModelUsagePreference>>();
|
||||
|
||||
match serde_json::to_string(&providers_with_usage) {
|
||||
Ok(json) => {
|
||||
let body = Full::new(Bytes::from(json))
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
Response::builder()
|
||||
.status(StatusCode::OK)
|
||||
.header("Content-Type", "application/json")
|
||||
.body(body)
|
||||
.unwrap()
|
||||
}
|
||||
Err(_) => {
|
||||
let body = Full::new(Bytes::from_static(
|
||||
b"{\"error\":\"Failed to serialize models\"}",
|
||||
))
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
Response::builder()
|
||||
.status(StatusCode::INTERNAL_SERVER_ERROR)
|
||||
.header("Content-Type", "application/json")
|
||||
.body(body)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn update_preferences(
|
||||
request: Request<hyper::body::Incoming>,
|
||||
llm_providers: Arc<tokio::sync::RwLock<Vec<LlmProvider>>>,
|
||||
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
|
||||
let request_body = request.collect().await?.to_bytes();
|
||||
|
||||
let usage: Vec<ModelUsagePreference> = match serde_json::from_slice(&request_body) {
|
||||
Ok(usage) => usage,
|
||||
Err(_) => {
|
||||
let response_body = Full::new(Bytes::from_static(b"Invalid request body: "))
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
return Ok(Response::builder()
|
||||
.status(StatusCode::BAD_REQUEST)
|
||||
.header("Content-Type", "text/plain")
|
||||
.body(response_body)
|
||||
.unwrap());
|
||||
}
|
||||
};
|
||||
|
||||
let usage_model_map: HashMap<String, ModelUsagePreference> =
|
||||
usage.into_iter().map(|u| (u.model.clone(), u)).collect();
|
||||
|
||||
info!(
|
||||
"Updating usage preferences for models: {:?}",
|
||||
usage_model_map.keys()
|
||||
);
|
||||
|
||||
let mut llm_providers = llm_providers.write().await;
|
||||
|
||||
// ensure that models coming in the request are valid
|
||||
let llm_provider_names: Vec<String> = llm_providers
|
||||
.iter()
|
||||
.map(|provider| provider.name.clone())
|
||||
.collect();
|
||||
|
||||
for model in usage_model_map.keys() {
|
||||
if !llm_provider_names.contains(model) {
|
||||
let model_not_found = format!("model not found: {}", model);
|
||||
warn!("updating preferences: {}", model_not_found);
|
||||
let response_body = Full::new(model_not_found.into())
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
return Ok(Response::builder()
|
||||
.status(StatusCode::BAD_REQUEST)
|
||||
.header("Content-Type", "text/plain")
|
||||
.body(response_body)
|
||||
.unwrap());
|
||||
}
|
||||
}
|
||||
|
||||
let mut updated_models_list = Vec::new();
|
||||
for provider in llm_providers.iter_mut() {
|
||||
if let Some(usage_provider) = usage_model_map.get(&provider.name) {
|
||||
provider.usage = usage_provider.usage.clone();
|
||||
updated_models_list.push(ModelUsagePreference {
|
||||
name: provider.name.clone(),
|
||||
model: provider.model.clone().unwrap_or_default(),
|
||||
usage: provider.usage.clone(),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
if !updated_models_list.is_empty() {
|
||||
// return list of updated models
|
||||
let response_body = Full::new(Bytes::from(format!(
|
||||
"{{\"updated_models\": {}}}",
|
||||
serde_json::to_string(&updated_models_list).unwrap()
|
||||
)))
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
Ok(Response::builder()
|
||||
.status(StatusCode::OK)
|
||||
.header("Content-Type", "application/json")
|
||||
.body(response_body)
|
||||
.unwrap())
|
||||
} else {
|
||||
let response_body = Full::new(Bytes::from_static(b"Provider not found"))
|
||||
.map_err(|never| match never {})
|
||||
.boxed();
|
||||
Ok(Response::builder()
|
||||
.status(StatusCode::NOT_FOUND)
|
||||
.header("Content-Type", "text/plain")
|
||||
.body(response_body)
|
||||
.unwrap())
|
||||
}
|
||||
}
|
||||
|
|
@ -1,6 +1,5 @@
|
|||
use brightstaff::handlers::chat_completions::chat_completions;
|
||||
use brightstaff::handlers::models::list_models;
|
||||
use brightstaff::handlers::preferences::{list_preferences, update_preferences};
|
||||
use brightstaff::router::llm_router::RouterService;
|
||||
use brightstaff::utils::tracing::init_tracer;
|
||||
use bytes::Bytes;
|
||||
|
|
@ -116,12 +115,6 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
|
|||
.with_context(parent_cx)
|
||||
.await
|
||||
}
|
||||
(&Method::GET, "/v1/router/preferences") => {
|
||||
Ok(list_preferences(llm_providers).await)
|
||||
}
|
||||
(&Method::PUT, "/v1/router/preferences") => {
|
||||
update_preferences(req, llm_providers).await
|
||||
}
|
||||
(&Method::GET, "/v1/models") => Ok(list_models(llm_providers).await),
|
||||
(&Method::OPTIONS, "/v1/models") => {
|
||||
let mut response = Response::new(empty());
|
||||
|
|
|
|||
|
|
@ -73,7 +73,7 @@ impl RouterModel for RouterModelV1 {
|
|||
fn generate_request(
|
||||
&self,
|
||||
messages: &[Message],
|
||||
usage_preferences: &Option<Vec<ModelUsagePreference>>,
|
||||
usage_preferences_from_request: &Option<Vec<ModelUsagePreference>>,
|
||||
) -> ChatCompletionsRequest {
|
||||
// remove system prompt, tool calls, tool call response and messages without content
|
||||
// if content is empty its likely a tool call
|
||||
|
|
@ -150,31 +150,17 @@ impl RouterModel for RouterModelV1 {
|
|||
})
|
||||
.collect::<Vec<Message>>();
|
||||
|
||||
let llm_route_json = usage_preferences
|
||||
.as_ref()
|
||||
.map(|prefs| {
|
||||
let llm_route: Vec<RoutingPreference> = prefs
|
||||
.iter()
|
||||
.map(|pref| RoutingPreference {
|
||||
name: pref.name.clone(),
|
||||
description: pref.usage.clone().unwrap_or_default(),
|
||||
})
|
||||
.collect();
|
||||
serde_json::to_string(&llm_route).unwrap_or_default()
|
||||
})
|
||||
.unwrap_or_else(|| self.llm_route_json_str.clone());
|
||||
|
||||
let messages_content = ARCH_ROUTER_V1_SYSTEM_PROMPT
|
||||
.replace("{routes}", &llm_route_json)
|
||||
.replace(
|
||||
"{conversation}",
|
||||
&serde_json::to_string(&selected_conversation_list).unwrap_or_default(),
|
||||
);
|
||||
// Generate the router request message based on the usage preferences.
|
||||
// If preferences are passed in request then we use them otherwise we use the default routing model preferences.
|
||||
let router_message = match convert_to_router_preferences(usage_preferences_from_request) {
|
||||
Some(prefs) => generate_router_message(&prefs, &selected_conversation_list),
|
||||
None => generate_router_message(&self.llm_route_json_str, &selected_conversation_list),
|
||||
};
|
||||
|
||||
ChatCompletionsRequest {
|
||||
model: self.routing_model.clone(),
|
||||
messages: vec![Message {
|
||||
content: Some(ContentType::Text(messages_content)),
|
||||
content: Some(ContentType::Text(router_message)),
|
||||
role: USER_ROLE.to_string(),
|
||||
}],
|
||||
temperature: Some(0.01),
|
||||
|
|
@ -201,12 +187,18 @@ impl RouterModel for RouterModelV1 {
|
|||
|
||||
if let Some(usage_preferences) = usage_preferences {
|
||||
// If usage preferences are defined, we need to find the model that matches the selected route
|
||||
let matching_preference = usage_preferences
|
||||
let model_name: Option<String> = usage_preferences
|
||||
.iter()
|
||||
.find(|pref| pref.name == selected_route);
|
||||
.map(|pref| {
|
||||
pref.routing_preferences
|
||||
.iter()
|
||||
.find(|routing_pref| routing_pref.name == selected_route)
|
||||
.map(|_| pref.model.clone())
|
||||
})
|
||||
.find_map(|model| model);
|
||||
|
||||
if let Some(preference) = matching_preference {
|
||||
return Ok(Some((selected_route, preference.model.clone())));
|
||||
if let Some(model_name) = model_name {
|
||||
return Ok(Some((selected_route, model_name)));
|
||||
} else {
|
||||
warn!(
|
||||
"No matching model found for route: {}, usage preferences: {:?}",
|
||||
|
|
@ -216,7 +208,7 @@ impl RouterModel for RouterModelV1 {
|
|||
}
|
||||
}
|
||||
|
||||
// If no usage preferences are defined, we return the route with the routing model
|
||||
// If no usage preferences are passed in request then use the default routing model preferences
|
||||
if let Some(model) = self.llm_route_to_model_map.get(&selected_route).cloned() {
|
||||
return Ok(Some((selected_route, model)));
|
||||
}
|
||||
|
|
@ -234,6 +226,37 @@ impl RouterModel for RouterModelV1 {
|
|||
}
|
||||
}
|
||||
|
||||
fn generate_router_message(prefs: &str, selected_conversation_list: &Vec<Message>) -> String {
|
||||
ARCH_ROUTER_V1_SYSTEM_PROMPT
|
||||
.replace("{routes}", prefs)
|
||||
.replace(
|
||||
"{conversation}",
|
||||
&serde_json::to_string(&selected_conversation_list).unwrap_or_default(),
|
||||
)
|
||||
}
|
||||
|
||||
fn convert_to_router_preferences(
|
||||
prefs_from_request: &Option<Vec<ModelUsagePreference>>,
|
||||
) -> Option<String> {
|
||||
if let Some(usage_preferences) = prefs_from_request {
|
||||
let routing_preferences = usage_preferences
|
||||
.iter()
|
||||
.flat_map(|pref| {
|
||||
pref.routing_preferences
|
||||
.iter()
|
||||
.map(|routing_pref| RoutingPreference {
|
||||
name: routing_pref.name.clone(),
|
||||
description: routing_pref.description.clone(),
|
||||
})
|
||||
})
|
||||
.collect::<Vec<RoutingPreference>>();
|
||||
|
||||
return Some(serde_json::to_string(&routing_preferences).unwrap_or_default());
|
||||
}
|
||||
|
||||
None
|
||||
}
|
||||
|
||||
fn fix_json_response(body: &str) -> String {
|
||||
let mut updated_body = body.to_string();
|
||||
|
||||
|
|
@ -299,7 +322,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), usize::MAX);
|
||||
|
||||
|
|
@ -356,7 +380,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), usize::MAX);
|
||||
|
||||
|
|
@ -379,9 +404,11 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
let conversation: Vec<Message> = serde_json::from_str(conversation_str).unwrap();
|
||||
|
||||
let usage_preferences = Some(vec![ModelUsagePreference {
|
||||
name: "code-generation".to_string(),
|
||||
model: "claude/claude-3-7-sonnet".to_string(),
|
||||
usage: Some("generating new code snippets, functions, or boilerplate based on user prompts or requirements".to_string()),
|
||||
routing_preferences: vec![RoutingPreference {
|
||||
name: "code-generation".to_string(),
|
||||
description: "generating new code snippets, functions, or boilerplate based on user prompts or requirements".to_string(),
|
||||
}],
|
||||
}]);
|
||||
let req = router.generate_request(&conversation, &usage_preferences);
|
||||
|
||||
|
|
@ -419,7 +446,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), 235);
|
||||
|
||||
|
|
@ -478,7 +506,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), 200);
|
||||
|
|
@ -538,7 +567,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), 230);
|
||||
|
||||
|
|
@ -604,7 +634,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), usize::MAX);
|
||||
|
||||
|
|
@ -672,7 +703,8 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let routing_model = "test-model".to_string();
|
||||
let router = RouterModelV1::new(llm_routes, routing_model.clone(), usize::MAX);
|
||||
|
||||
|
|
@ -747,14 +779,18 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
]
|
||||
}
|
||||
"#;
|
||||
let llm_routes = serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
let llm_routes =
|
||||
serde_json::from_str::<HashMap<String, Vec<RoutingPreference>>>(routes_str).unwrap();
|
||||
|
||||
let router = RouterModelV1::new(llm_routes, "test-model".to_string(), 2000);
|
||||
|
||||
// Case 1: Valid JSON with non-empty route
|
||||
let input = r#"{"route": "Image generation"}"#;
|
||||
let result = router.parse_response(input, &None).unwrap();
|
||||
assert_eq!(result, Some(("Image generation".to_string(), "gpt-4o".to_string())));
|
||||
assert_eq!(
|
||||
result,
|
||||
Some(("Image generation".to_string(), "gpt-4o".to_string()))
|
||||
);
|
||||
|
||||
// Case 2: Valid JSON with empty route
|
||||
let input = r#"{"route": ""}"#;
|
||||
|
|
@ -784,11 +820,17 @@ Based on your analysis, provide your response in the following JSON formats if y
|
|||
// Case 6: Single quotes and \n in JSON
|
||||
let input = "{'route': 'Image generation'}\\n";
|
||||
let result = router.parse_response(input, &None).unwrap();
|
||||
assert_eq!(result, Some(("Image generation".to_string(), "gpt-4o".to_string())));
|
||||
assert_eq!(
|
||||
result,
|
||||
Some(("Image generation".to_string(), "gpt-4o".to_string()))
|
||||
);
|
||||
|
||||
// Case 7: Code block marker
|
||||
let input = "```json\n{\"route\": \"Image generation\"}\n```";
|
||||
let result = router.parse_response(input, &None).unwrap();
|
||||
assert_eq!(result, Some(("Image generation".to_string(), "gpt-4o".to_string())));
|
||||
assert_eq!(
|
||||
result,
|
||||
Some(("Image generation".to_string(), "gpt-4o".to_string()))
|
||||
);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
use hermesllm::providers::openai::types::{ModelDetail, ModelObject, Models};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use serde_with::skip_serializing_none;
|
||||
use std::collections::HashMap;
|
||||
use std::fmt::Display;
|
||||
|
||||
|
|
@ -178,12 +177,10 @@ impl Display for LlmProviderType {
|
|||
}
|
||||
}
|
||||
|
||||
#[skip_serializing_none]
|
||||
#[derive(Serialize, Deserialize, Debug)]
|
||||
pub struct ModelUsagePreference {
|
||||
pub name: String,
|
||||
pub model: String,
|
||||
pub usage: Option<String>,
|
||||
pub routing_preferences: Vec<RoutingPreference>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
|
|
|
|||
|
|
@ -35,9 +35,16 @@ pub enum MultiPartContentType {
|
|||
ImageUrl,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
|
||||
pub struct ImageUrl {
|
||||
pub url: String,
|
||||
}
|
||||
|
||||
#[skip_serializing_none]
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
|
||||
pub struct MultiPartContent {
|
||||
pub text: Option<String>,
|
||||
pub image_url: Option<ImageUrl>,
|
||||
#[serde(rename = "type")]
|
||||
pub content_type: MultiPartContentType,
|
||||
}
|
||||
|
|
@ -307,10 +314,12 @@ mod tests {
|
|||
MultiPartContent {
|
||||
text: Some("This is a text part.".to_string()),
|
||||
content_type: MultiPartContentType::Text,
|
||||
image_url: None,
|
||||
},
|
||||
MultiPartContent {
|
||||
text: Some("https://example.com/image.png".to_string()),
|
||||
content_type: MultiPartContentType::ImageUrl,
|
||||
image_url: None,
|
||||
},
|
||||
]);
|
||||
assert_eq!(multi_part_content.to_string(), "This is a text part.");
|
||||
|
|
@ -364,6 +373,61 @@ mod tests {
|
|||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_chat_completions_request_image_content() {
|
||||
const CHAT_COMPLETIONS_REQUEST: &str = r#"
|
||||
{
|
||||
"stream": true,
|
||||
"model": "openai/gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "describe this photo pls"
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/...=="
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}"#;
|
||||
|
||||
let chat_completions_request: ChatCompletionsRequest =
|
||||
serde_json::from_str(CHAT_COMPLETIONS_REQUEST).unwrap();
|
||||
assert_eq!(chat_completions_request.model, "openai/gpt-4o");
|
||||
if let Some(ContentType::MultiPart(multi_part_content)) =
|
||||
chat_completions_request.messages[0].content.as_ref()
|
||||
{
|
||||
assert_eq!(multi_part_content.len(), 2);
|
||||
assert_eq!(
|
||||
multi_part_content[0].content_type,
|
||||
MultiPartContentType::Text
|
||||
);
|
||||
assert_eq!(
|
||||
multi_part_content[0].text,
|
||||
Some("describe this photo pls".to_string())
|
||||
);
|
||||
assert_eq!(
|
||||
multi_part_content[1].content_type,
|
||||
MultiPartContentType::ImageUrl
|
||||
);
|
||||
assert_eq!(
|
||||
multi_part_content[1].image_url,
|
||||
Some(ImageUrl {
|
||||
url: "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/...==".to_string(),
|
||||
})
|
||||
);
|
||||
} else {
|
||||
panic!("Expected MultiPartContent");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_sse_streaming() {
|
||||
let json_data = r#"data: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1700000000,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
|
||||
|
|
|
|||
|
|
@ -12,6 +12,9 @@ llm_providers:
|
|||
- access_key: $OPENAI_API_KEY
|
||||
model: openai/gpt-4o-mini
|
||||
|
||||
- access_key: $OPENAI_API_KEY
|
||||
model: openai/gpt-4.1
|
||||
|
||||
- access_key: $OPENAI_API_KEY
|
||||
model: openai/gpt-4o
|
||||
default: true
|
||||
|
|
|
|||
|
|
@ -14,9 +14,9 @@ Make sure your machine is up to date with [latest version of archgw]([url](https
|
|||
2. start archgw in the foreground
|
||||
```bash
|
||||
(venv) $ archgw up --service archgw --foreground
|
||||
2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.4
|
||||
2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.7
|
||||
2025-05-30 18:00:09,953 - cli.main - INFO - Validating /Users/adilhafeez/src/intelligent-prompt-gateway/demos/use_cases/preference_based_routing/arch_config.yaml
|
||||
2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.4
|
||||
2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.7
|
||||
2025-05-30 18:00:10,662 - cli.core - INFO - archgw status: running, health status: starting
|
||||
2025-05-30 18:00:11,712 - cli.core - INFO - archgw status: running, health status: starting
|
||||
2025-05-30 18:00:12,761 - cli.core - INFO - archgw is running and is healthy!
|
||||
|
|
|
|||
|
|
@ -38,29 +38,29 @@ Adding custom LLM Provider
|
|||
We support any OpenAI compliant LLM for example mistral, openai, ollama etc. We also offer first class support for OpenAI, Anthropic, DeepSeek, Mistral, Groq, and Ollama based models.
|
||||
You can easily configure an LLM that communicates over the OpenAI API interface, by following the below guide.
|
||||
|
||||
For example following code block shows you how to add an ollama-supported LLM in the `arch_config.yaml` file.
|
||||
For example following code block shows you how to add an ollama-supported LLM in the ``arch_config.yaml`` file.
|
||||
|
||||
.. code-block:: yaml
|
||||
|
||||
llm_providers:
|
||||
- model: some_custom_llm_provider/llama3.2
|
||||
provider_interface: openai
|
||||
base_url: http://host.docker.internal:11434
|
||||
- model: some_custom_llm_provider/llama3.2
|
||||
provider_interface: openai
|
||||
base_url: http://host.docker.internal:11434
|
||||
|
||||
And in the following code block shows you how to add mistral llm provider in the `arch_config.yaml` file.
|
||||
And in the following code block shows you how to add mistral llm provider in the ``arch_config.yaml`` file.
|
||||
|
||||
.. code-block:: yaml
|
||||
|
||||
llm_providers:
|
||||
- name: mistral/ministral-3b-latest
|
||||
access_key: $MISTRAL_API_KEY
|
||||
- name: mistral/ministral-3b-latest
|
||||
access_key: $MISTRAL_API_KEY
|
||||
|
||||
Example: Using the OpenAI Python SDK
|
||||
------------------------------------
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from openai import OpenAI
|
||||
from openai import OpenAI
|
||||
|
||||
# Initialize the Arch client
|
||||
client = OpenAI(base_url="http://127.0.0.1:2000/")
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ from sphinxawesome_theme.postprocess import Icons
|
|||
project = "Arch Docs"
|
||||
copyright = "2025, Katanemo Labs, Inc"
|
||||
author = "Katanemo Labs, Inc"
|
||||
release = " v0.3.4"
|
||||
release = " v0.3.7"
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
|
|||
|
||||
$ python -m venv venv
|
||||
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
|
||||
$ pip install archgw==0.3.4
|
||||
$ pip install archgw==0.3.7
|
||||
|
||||
|
||||
Build AI Agent with Arch Gateway
|
||||
|
|
|
|||
197
model_server/poetry.lock
generated
197
model_server/poetry.lock
generated
|
|
@ -2,13 +2,13 @@
|
|||
|
||||
[[package]]
|
||||
name = "accelerate"
|
||||
version = "1.8.1"
|
||||
version = "1.9.0"
|
||||
description = "Accelerate"
|
||||
optional = false
|
||||
python-versions = ">=3.9.0"
|
||||
files = [
|
||||
{file = "accelerate-1.8.1-py3-none-any.whl", hash = "sha256:c47b8994498875a2b1286e945bd4d20e476956056c7941d512334f4eb44ff991"},
|
||||
{file = "accelerate-1.8.1.tar.gz", hash = "sha256:f60df931671bc4e75077b852990469d4991ce8bd3a58e72375c3c95132034db9"},
|
||||
{file = "accelerate-1.9.0-py3-none-any.whl", hash = "sha256:c24739a97ade1d54af4549a65f8b6b046adc87e2b3e4d6c66516e32c53d5a8f1"},
|
||||
{file = "accelerate-1.9.0.tar.gz", hash = "sha256:0e8c61f81af7bf37195b6175a545ed292617dd90563c88f49020aea5b6a0b47f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -29,7 +29,7 @@ sagemaker = ["sagemaker"]
|
|||
test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
|
||||
test-fp8 = ["torchao"]
|
||||
test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
|
||||
test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "wandb"]
|
||||
test-trackers = ["comet-ml", "dvclive", "matplotlib", "mlflow", "swanlab", "tensorboard", "trackio", "wandb"]
|
||||
testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -82,15 +82,26 @@ typing_extensions = {version = ">=4", markers = "python_version < \"3.11\""}
|
|||
[package.extras]
|
||||
tests = ["mypy (>=1.14.0)", "pytest", "pytest-asyncio"]
|
||||
|
||||
[[package]]
|
||||
name = "backports-asyncio-runner"
|
||||
version = "1.2.0"
|
||||
description = "Backport of asyncio.Runner, a context manager that controls event loop life cycle."
|
||||
optional = false
|
||||
python-versions = "<3.11,>=3.8"
|
||||
files = [
|
||||
{file = "backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5"},
|
||||
{file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.7.9"
|
||||
version = "2025.7.14"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "certifi-2025.7.9-py3-none-any.whl", hash = "sha256:d842783a14f8fdd646895ac26f719a061408834473cfc10203f6a575beb15d39"},
|
||||
{file = "certifi-2025.7.9.tar.gz", hash = "sha256:c1d2ec05395148ee10cf672ffc28cd37ea0ab0d99f9cc74c43e588cbd111b079"},
|
||||
{file = "certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2"},
|
||||
{file = "certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -324,13 +335,13 @@ typing = ["typing-extensions (>=4.12.2)"]
|
|||
|
||||
[[package]]
|
||||
name = "fsspec"
|
||||
version = "2025.5.1"
|
||||
version = "2025.7.0"
|
||||
description = "File-system specification"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "fsspec-2025.5.1-py3-none-any.whl", hash = "sha256:24d3a2e663d5fc735ab256263c4075f374a174c3410c0b25e5bd1970bceaa462"},
|
||||
{file = "fsspec-2025.5.1.tar.gz", hash = "sha256:2e55e47a540b91843b755e83ded97c6e897fa0942b11490113f09e9c443c2475"},
|
||||
{file = "fsspec-2025.7.0-py3-none-any.whl", hash = "sha256:8b012e39f63c7d5f10474de957f3ab793b47b45ae7d39f2fb735f8bbe25c0e21"},
|
||||
{file = "fsspec-2025.7.0.tar.gz", hash = "sha256:786120687ffa54b8283d942929540d8bc5ccfa820deb555a2b5d0ed2b737bf58"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -338,7 +349,7 @@ abfs = ["adlfs"]
|
|||
adl = ["adlfs"]
|
||||
arrow = ["pyarrow (>=1)"]
|
||||
dask = ["dask", "distributed"]
|
||||
dev = ["pre-commit", "ruff"]
|
||||
dev = ["pre-commit", "ruff (>=0.5)"]
|
||||
doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
|
||||
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
|
||||
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
|
||||
|
|
@ -380,66 +391,66 @@ grpc = ["grpcio (>=1.44.0,<2.0.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.73.1"
|
||||
version = "1.74.0"
|
||||
description = "HTTP/2-based RPC framework"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "grpcio-1.73.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:2d70f4ddd0a823436c2624640570ed6097e40935c9194482475fe8e3d9754d55"},
|
||||
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|
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|
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||||
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||||
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||||
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]
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.73.1)"]
|
||||
protobuf = ["grpcio-tools (>=1.74.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
|
|
@ -520,19 +531,19 @@ zstd = ["zstandard (>=0.18.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.33.4"
|
||||
version = "0.34.1"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.33.4-py3-none-any.whl", hash = "sha256:09f9f4e7ca62547c70f8b82767eefadd2667f4e116acba2e3e62a5a81815a7bb"},
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]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
fsspec = ">=2023.5.0"
|
||||
hf-xet = {version = ">=1.1.2,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
|
||||
hf-xet = {version = ">=1.1.3,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
|
||||
packaging = ">=20.9"
|
||||
pyyaml = ">=5.1"
|
||||
requests = "*"
|
||||
|
|
@ -540,16 +551,16 @@ tqdm = ">=4.42.1"
|
|||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
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|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (==1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
|
||||
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
|
||||
hf-transfer = ["hf-transfer (>=0.1.4)"]
|
||||
hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
|
||||
inference = ["aiohttp"]
|
||||
mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
|
||||
oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
|
||||
quality = ["libcst (==1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "ruff (>=0.9.0)"]
|
||||
quality = ["libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "ruff (>=0.9.0)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
|
|
@ -1023,13 +1034,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.95.1"
|
||||
version = "1.97.1"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "openai-1.95.1-py3-none-any.whl", hash = "sha256:8bbdfeceef231b1ddfabbc232b179d79f8b849aab5a7da131178f8d10e0f162f"},
|
||||
{file = "openai-1.95.1.tar.gz", hash = "sha256:f089b605282e2a2b6776090b4b46563ac1da77f56402a222597d591e2dcc1086"},
|
||||
{file = "openai-1.97.1-py3-none-any.whl", hash = "sha256:4e96bbdf672ec3d44968c9ea39d2c375891db1acc1794668d8149d5fa6000606"},
|
||||
{file = "openai-1.97.1.tar.gz", hash = "sha256:a744b27ae624e3d4135225da9b1c89c107a2a7e5bc4c93e5b7b5214772ce7a4e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1498,16 +1509,17 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "requests
|
|||
|
||||
[[package]]
|
||||
name = "pytest-asyncio"
|
||||
version = "1.0.0"
|
||||
version = "1.1.0"
|
||||
description = "Pytest support for asyncio"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pytest_asyncio-1.0.0-py3-none-any.whl", hash = "sha256:4f024da9f1ef945e680dc68610b52550e36590a67fd31bb3b4943979a1f90ef3"},
|
||||
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|
||||
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|
||||
{file = "pytest_asyncio-1.1.0.tar.gz", hash = "sha256:796aa822981e01b68c12e4827b8697108f7205020f24b5793b3c41555dab68ea"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
backports-asyncio-runner = {version = ">=1.1,<2", markers = "python_version < \"3.11\""}
|
||||
pytest = ">=8.2,<9"
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -2019,18 +2031,18 @@ telegram = ["requests"]
|
|||
|
||||
[[package]]
|
||||
name = "transformers"
|
||||
version = "4.53.2"
|
||||
version = "4.54.0"
|
||||
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
|
||||
optional = false
|
||||
python-versions = ">=3.9.0"
|
||||
files = [
|
||||
{file = "transformers-4.53.2-py3-none-any.whl", hash = "sha256:db8f4819bb34f000029c73c3c557e7d06fc1b8e612ec142eecdae3947a9c78bf"},
|
||||
{file = "transformers-4.53.2.tar.gz", hash = "sha256:6c3ed95edfb1cba71c4245758f1b4878c93bf8cde77d076307dacb2cbbd72be2"},
|
||||
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|
||||
{file = "transformers-4.54.0.tar.gz", hash = "sha256:843da4d66a573cef3d1b2e7a1d767e77da054621e69d9f3faff761e55a1f8203"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
huggingface-hub = ">=0.30.0,<1.0"
|
||||
huggingface-hub = ">=0.34.0,<1.0"
|
||||
numpy = ">=1.17"
|
||||
packaging = ">=20.0"
|
||||
pyyaml = ">=5.1"
|
||||
|
|
@ -2042,15 +2054,15 @@ tqdm = ">=4.27"
|
|||
|
||||
[package.extras]
|
||||
accelerate = ["accelerate (>=0.26.0)"]
|
||||
all = ["Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "codecarbon (>=2.8.1)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<0.7)", "librosa", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision"]
|
||||
all = ["Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "codecarbon (>=2.8.1)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<0.7)", "librosa", "mistral-common[opencv] (>=1.6.3)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision"]
|
||||
audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
benchmark = ["optimum-benchmark (>=0.3.0)"]
|
||||
codecarbon = ["codecarbon (>=2.8.1)"]
|
||||
deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
|
||||
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
dev = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<0.7)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<0.7)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
dev = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.6.1,<0.7)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev-tensorflow = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "onnxconverter-common", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<0.7)", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "pandas (<2.3.0)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
|
|
@ -2058,6 +2070,7 @@ hf-xet = ["hf_xet"]
|
|||
hub-kernels = ["kernels (>=0.6.1,<0.7)"]
|
||||
integrations = ["kernels (>=0.6.1,<0.7)", "optuna", "ray[tune] (>=2.7.0)", "sigopt"]
|
||||
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)"]
|
||||
mistral-common = ["mistral-common[opencv] (>=1.6.3)"]
|
||||
modelcreation = ["cookiecutter (==1.7.3)"]
|
||||
natten = ["natten (>=0.14.6,<0.15.0)"]
|
||||
num2words = ["num2words"]
|
||||
|
|
@ -2065,27 +2078,27 @@ onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1
|
|||
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
|
||||
open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
|
||||
optuna = ["optuna"]
|
||||
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
|
||||
quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
|
||||
ray = ["ray[tune] (>=2.7.0)"]
|
||||
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
|
||||
retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
|
||||
ruff = ["ruff (==0.11.2)"]
|
||||
sagemaker = ["sagemaker (>=2.31.0)"]
|
||||
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
|
||||
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
|
||||
serving = ["accelerate (>=0.26.0)", "fastapi", "openai", "pydantic (>=2)", "starlette", "torch (>=2.1)", "uvicorn"]
|
||||
sigopt = ["sigopt"]
|
||||
sklearn = ["scikit-learn"]
|
||||
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
tiktoken = ["blobfile", "tiktoken"]
|
||||
timm = ["timm (<=1.0.11)"]
|
||||
timm = ["timm (!=1.0.18,<=1.0.19)"]
|
||||
tokenizers = ["tokenizers (>=0.21,<0.22)"]
|
||||
torch = ["accelerate (>=0.26.0)", "torch (>=2.1)"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.30.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.1)", "tqdm (>=4.27)"]
|
||||
video = ["av"]
|
||||
vision = ["Pillow (>=10.0.1,<=15.0)"]
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "archgw_modelserver"
|
||||
version = "0.3.4"
|
||||
version = "0.3.7"
|
||||
description = "A model server for serving models"
|
||||
authors = ["Katanemo Labs, Inc <info@katanemo.com>"]
|
||||
license = "Apache 2.0"
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ def start_server(port=51000, foreground=False):
|
|||
if foreground:
|
||||
process = subprocess.Popen(
|
||||
[
|
||||
"python",
|
||||
sys.executable,
|
||||
"-m",
|
||||
"uvicorn",
|
||||
"src.main:app",
|
||||
|
|
@ -85,7 +85,7 @@ def start_server(port=51000, foreground=False):
|
|||
else:
|
||||
process = subprocess.Popen(
|
||||
[
|
||||
"python",
|
||||
sys.executable,
|
||||
"-m",
|
||||
"uvicorn",
|
||||
"src.main:app",
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue